{"product_id":"meta-heuristic-and-evolutionary-algorithms-for-engineering-optimization-isbn-9781119386995","title":"Meta-heuristic and Evolutionary Algorithms for Engineering Optimization","description":"\u003cp\u003e\u003cb\u003eA detailed review of a wide range of meta-heuristic and evolutionary algorithms in a systematic manner and how they relate to engineering optimization problems\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis book introduces the main metaheuristic algorithms and their applications in optimization. It describes 20 leading meta-heuristic and evolutionary algorithms and presents discussions and assessments of their performance in solving optimization problems from several fields of engineering. The book features clear and concise principles and presents detailed descriptions of leading methods such as the pattern search (PS) algorithm, the genetic algorithm (GA), the simulated annealing (SA) algorithm, the Tabu search (TS) algorithm, the ant colony optimization (ACO), and the particle swarm optimization (PSO) technique.\u003c\/p\u003e \u003cp\u003eChapter 1 of \u003ci\u003eMeta-heuristic and Evolutionary Algorithms for Engineering Optimization \u003c\/i\u003eprovides an overview of optimization and defines it by presenting examples of optimization problems in different engineering domains. Chapter 2 presents an introduction to meta-heuristic and evolutionary algorithms and links them to engineering problems. Chapters 3 to 22 are each devoted to a separate algorithm— and they each start with a brief literature review of the development of the algorithm, and its applications to engineering problems. The principles, steps, and execution of the algorithms are described in detail, and a pseudo code of the algorithm is presented, which serves as a guideline for coding the algorithm to solve specific applications. This book:\u003c\/p\u003e \u003cul style=\"line-height: 25px; margin-left: 15px; margin-top: 0px; font-family: Arial; font-size: 13.3333px; background-color: #f7f3e7;\"\u003e \u003cli\u003eIntroduces state-of-the-art metaheuristic algorithms and their applications to engineering optimization;\u003c\/li\u003e \u003cli\u003eFills a gap in the current literature by compiling and explaining the various meta-heuristic and evolutionary algorithms in a clear and systematic manner;\u003c\/li\u003e \u003cli\u003eProvides a step-by-step presentation of each algorithm and guidelines for practical implementation and coding of algorithms;\u003c\/li\u003e \u003cli\u003eDiscusses and assesses the performance of metaheuristic algorithms in multiple problems from many fields of engineering;\u003c\/li\u003e \u003cli\u003eRelates optimization algorithms to engineering problems employing a unifying approach.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eMeta-heuristic and Evolutionary Algorithms for Engineering Optimization \u003c\/i\u003eis a reference intended for students, engineers, researchers, and instructors in the fields of industrial engineering, operations research, optimization\/mathematics, engineering optimization, and computer science.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eOMID BOZORG-HADDAD, PhD, \u003c\/b\u003eis Professor in the Department of Irrigation and Reclamation Engineering at the University of Tehran, Iran.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eMOHAMMAD SOLGI, M.Sc., \u003c\/b\u003eis Teacher Assistant for M.Sc. courses at the University of Tehran, Iran.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eHUGO A. LOÁICIGA, PhD, \u003c\/b\u003eis Professor in the Department of Geography at the University of California, Santa Barbara, United States of America.\u003c\/p\u003e \u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003eAbout the Authors xvii\u003c\/p\u003e \u003cp\u003eList of Figures xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Overview of Optimization 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 1\u003c\/p\u003e \u003cp\u003e1.1 Optimization 1\u003c\/p\u003e \u003cp\u003e1.1.1 Objective Function 2\u003c\/p\u003e \u003cp\u003e1.1.2 Decision Variables 2\u003c\/p\u003e \u003cp\u003e1.1.3 Solutions of an Optimization Problem 3\u003c\/p\u003e \u003cp\u003e1.1.4 Decision Space 3\u003c\/p\u003e \u003cp\u003e1.1.5 Constraints or Restrictions 3\u003c\/p\u003e \u003cp\u003e1.1.6 State Variables 3\u003c\/p\u003e \u003cp\u003e1.1.7 Local and Global Optima 4\u003c\/p\u003e \u003cp\u003e1.1.8 Near-Optimal Solutions 5\u003c\/p\u003e \u003cp\u003e1.1.9 Simulation 6\u003c\/p\u003e \u003cp\u003e1.2 Examples of the Formulation of Various Engineering Optimization Problems 7\u003c\/p\u003e \u003cp\u003e1.2.1 Mechanical Design 7\u003c\/p\u003e \u003cp\u003e1.2.2 Structural Design 9\u003c\/p\u003e \u003cp\u003e1.2.3 Electrical Engineering Optimization 10\u003c\/p\u003e \u003cp\u003e1.2.4 Water Resources Optimization 11\u003c\/p\u003e \u003cp\u003e1.2.5 Calibration of Hydrologic Models 13\u003c\/p\u003e \u003cp\u003e1.3 Conclusion 15\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Introduction to Meta\u003c\/b\u003e\u003cb\u003e-\u003c\/b\u003e\u003cb\u003eHeuristic and Evolutionary Algorithms 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 17\u003c\/p\u003e \u003cp\u003e2.1 Searching the Decision Space for Optimal Solutions 17\u003c\/p\u003e \u003cp\u003e2.2 Definition of Terms of Meta-Heuristic and Evolutionary Algorithms 21\u003c\/p\u003e \u003cp\u003e2.2.1 Initial State 21\u003c\/p\u003e \u003cp\u003e2.2.2 Iterations 21\u003c\/p\u003e \u003cp\u003e2.2.3 Final State 21\u003c\/p\u003e \u003cp\u003e2.2.4 Initial Data (Information) 21\u003c\/p\u003e \u003cp\u003e2.2.5 Decision Variables 22\u003c\/p\u003e \u003cp\u003e2.2.6 State Variables 23\u003c\/p\u003e \u003cp\u003e2.2.7 Objective Function 23\u003c\/p\u003e \u003cp\u003e2.2.8 Simulation Model 24\u003c\/p\u003e \u003cp\u003e2.2.9 Constraints 24\u003c\/p\u003e \u003cp\u003e2.2.10 Fitness Function 24\u003c\/p\u003e \u003cp\u003e2.3 Principles of Meta-Heuristic and Evolutionary Algorithms 25\u003c\/p\u003e \u003cp\u003e2.4 Classification of Meta-Heuristic and Evolutionary Algorithms 27\u003c\/p\u003e \u003cp\u003e2.4.1 Nature-Inspired and Non-Nature-Inspired Algorithms 27\u003c\/p\u003e \u003cp\u003e2.4.2 Population-Based and Single-Point Search Algorithms 28\u003c\/p\u003e \u003cp\u003e2.4.3 Memory-Based and Memory-Less Algorithms 28\u003c\/p\u003e \u003cp\u003e2.5 Meta-Heuristic and Evolutionary Algorithms in Discrete or Continuous Domains 28\u003c\/p\u003e \u003cp\u003e2.6 Generating Random Values of the Decision Variables 29\u003c\/p\u003e \u003cp\u003e2.7 Dealing with Constraints 29\u003c\/p\u003e \u003cp\u003e2.7.1 Removal Method 30\u003c\/p\u003e \u003cp\u003e2.7.2 Refinement Method 30\u003c\/p\u003e \u003cp\u003e2.7.3 Penalty Functions 31\u003c\/p\u003e \u003cp\u003e2.8 Fitness Function 33\u003c\/p\u003e \u003cp\u003e2.9 Selection of Solutions in Each Iteration 33\u003c\/p\u003e \u003cp\u003e2.10 Generating New Solutions 34\u003c\/p\u003e \u003cp\u003e2.11 The Best Solution in Each Algorithmic Iteration 35\u003c\/p\u003e \u003cp\u003e2.12 Termination Criteria 35\u003c\/p\u003e \u003cp\u003e2.13 General Algorithm 36\u003c\/p\u003e \u003cp\u003e2.14 Performance Evaluation of Meta-Heuristic and Evolutionary Algorithms 36\u003c\/p\u003e \u003cp\u003e2.15 Search Strategies 39\u003c\/p\u003e \u003cp\u003e2.16 Conclusion 41\u003c\/p\u003e \u003cp\u003eReferences 41\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Pattern Search 43\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 43\u003c\/p\u003e \u003cp\u003e3.1 Introduction 43\u003c\/p\u003e \u003cp\u003e3.2 Pattern Search (PS) Fundamentals 44\u003c\/p\u003e \u003cp\u003e3.3 Generating an Initial Solution 47\u003c\/p\u003e \u003cp\u003e3.4 Generating Trial Solutions 47\u003c\/p\u003e \u003cp\u003e3.4.1 Exploratory Move 47\u003c\/p\u003e \u003cp\u003e3.4.2 Pattern Move 49\u003c\/p\u003e \u003cp\u003e3.5 Updating the Mesh Size 50\u003c\/p\u003e \u003cp\u003e3.6 Termination Criteria 50\u003c\/p\u003e \u003cp\u003e3.7 User-Defined Parameters of the PS 51\u003c\/p\u003e \u003cp\u003e3.8 Pseudocode of the PS 51\u003c\/p\u003e \u003cp\u003e3.9 Conclusion 52\u003c\/p\u003e \u003cp\u003eReferences 52\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Genetic Algorithm 53\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 53\u003c\/p\u003e \u003cp\u003e4.1 Introduction 53\u003c\/p\u003e \u003cp\u003e4.2 Mapping the Genetic Algorithm (GA) to Natural Evolution 54\u003c\/p\u003e \u003cp\u003e4.3 Creating an Initial Population 56\u003c\/p\u003e \u003cp\u003e4.4 Selection of Parents to Create a New Generation 56\u003c\/p\u003e \u003cp\u003e4.4.1 Proportionate Selection 57\u003c\/p\u003e \u003cp\u003e4.4.2 Ranking Selection 58\u003c\/p\u003e \u003cp\u003e4.4.3 Tournament Selection 59\u003c\/p\u003e \u003cp\u003e4.5 Population Diversity and Selective Pressure 59\u003c\/p\u003e \u003cp\u003e4.6 Reproduction 59\u003c\/p\u003e \u003cp\u003e4.6.1 Crossover 60\u003c\/p\u003e \u003cp\u003e4.6.2 Mutation 62\u003c\/p\u003e \u003cp\u003e4.7 Termination Criteria 63\u003c\/p\u003e \u003cp\u003e4.8 User- Defined Parameters of the GA 63\u003c\/p\u003e \u003cp\u003e4.9 Pseudocode of the GA 64\u003c\/p\u003e \u003cp\u003e4.10 Conclusion 65\u003c\/p\u003e \u003cp\u003eReferences 65\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Simulated Annealing 69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 69\u003c\/p\u003e \u003cp\u003e5.1 Introduction 69\u003c\/p\u003e \u003cp\u003e5.2 Mapping the Simulated Annealing (SA) Algorithm to the Physical Annealing Process 70\u003c\/p\u003e \u003cp\u003e5.3 Generating an Initial State 72\u003c\/p\u003e \u003cp\u003e5.4 Generating a New State 72\u003c\/p\u003e \u003cp\u003e5.5 Acceptance Function 74\u003c\/p\u003e \u003cp\u003e5.6 Thermal Equilibrium 75\u003c\/p\u003e \u003cp\u003e5.7 Temperature Reduction 75\u003c\/p\u003e \u003cp\u003e5.8 Termination Criteria 76\u003c\/p\u003e \u003cp\u003e5.9 User- Defined Parameters of the SA 76\u003c\/p\u003e \u003cp\u003e5.10 Pseudocode of the SA 77\u003c\/p\u003e \u003cp\u003e5.11 Conclusion 77\u003c\/p\u003e \u003cp\u003eReferences 77\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Tabu Search 79\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 79\u003c\/p\u003e \u003cp\u003e6.1 Introduction 79\u003c\/p\u003e \u003cp\u003e6.2 Tabu Search (TS) Foundation 80\u003c\/p\u003e \u003cp\u003e6.3 Generating an Initial Searching Point 82\u003c\/p\u003e \u003cp\u003e6.4 Neighboring Points 82\u003c\/p\u003e \u003cp\u003e6.5 Tabu Lists 84\u003c\/p\u003e \u003cp\u003e6.6 Updating the Tabu List 84\u003c\/p\u003e \u003cp\u003e6.7 Attributive Memory 85\u003c\/p\u003e \u003cp\u003e6.7.1 Frequency-Based Memory 85\u003c\/p\u003e \u003cp\u003e6.7.2 Recency-Based Memory 85\u003c\/p\u003e \u003cp\u003e6.8 Aspiration Criteria 87\u003c\/p\u003e \u003cp\u003e6.9 Intensification and Diversification Strategies 87\u003c\/p\u003e \u003cp\u003e6.10 Termination Criteria 87\u003c\/p\u003e \u003cp\u003e6.11 User- Defined Parameters of the TS 87\u003c\/p\u003e \u003cp\u003e6.12 Pseudocode of the TS 88\u003c\/p\u003e \u003cp\u003e6.13 Conclusion 89\u003c\/p\u003e \u003cp\u003eReferences 89\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Ant Colony Optimization 91\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 91\u003c\/p\u003e \u003cp\u003e7.1 Introduction 91\u003c\/p\u003e \u003cp\u003e7.2 Mapping Ant Colony Optimization (ACO) to Ants’ Foraging Behavior 92\u003c\/p\u003e \u003cp\u003e7.3 Creating an Initial Population 94\u003c\/p\u003e \u003cp\u003e7.4 Allocating Pheromone to the Decision Space 96\u003c\/p\u003e \u003cp\u003e7.5 Generation of New Solutions 98\u003c\/p\u003e \u003cp\u003e7.6 Termination Criteria 99\u003c\/p\u003e \u003cp\u003e7.7 User- Defined Parameters of the ACO 99\u003c\/p\u003e \u003cp\u003e7.8 Pseudocode of the ACO 100\u003c\/p\u003e \u003cp\u003e7.9 Conclusion 100\u003c\/p\u003e \u003cp\u003eReferences 101\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Particle Swarm Optimization 103\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 103\u003c\/p\u003e \u003cp\u003e8.1 Introduction 103\u003c\/p\u003e \u003cp\u003e8.2 Mapping Particle Swarm Optimization (PSO) to the Social Behavior of Some Animals 104\u003c\/p\u003e \u003cp\u003e8.3 Creating an Initial Population of Particles 107\u003c\/p\u003e \u003cp\u003e8.4 The Individual and Global Best Positions 107\u003c\/p\u003e \u003cp\u003e8.5 Velocities of Particles 109\u003c\/p\u003e \u003cp\u003e8.6 Updating the Positions of Particles 110\u003c\/p\u003e \u003cp\u003e8.7 Termination Criteria 110\u003c\/p\u003e \u003cp\u003e8.8 User- Defined Parameters of the PSO 110\u003c\/p\u003e \u003cp\u003e8.9 Pseudocode of the PSO 111\u003c\/p\u003e \u003cp\u003e8.10 Conclusion 112\u003c\/p\u003e \u003cp\u003eReferences 112\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Differential Evolution 115\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 115\u003c\/p\u003e \u003cp\u003e9.1 Introduction 115\u003c\/p\u003e \u003cp\u003e9.2 Differential Evolution (DE) Fundamentals 116\u003c\/p\u003e \u003cp\u003e9.3 Creating an Initial Population 118\u003c\/p\u003e \u003cp\u003e9.4 Generating Trial Solutions 119\u003c\/p\u003e \u003cp\u003e9.4.1 Mutation 119\u003c\/p\u003e \u003cp\u003e9.4.2 Crossover 119\u003c\/p\u003e \u003cp\u003e9.5 Greedy Criteria 120\u003c\/p\u003e \u003cp\u003e9.6 Termination Criteria 120\u003c\/p\u003e \u003cp\u003e9.7 User-Defined Parameters of the DE 120\u003c\/p\u003e \u003cp\u003e9.8 Pseudocode of the DE 121\u003c\/p\u003e \u003cp\u003e9.9 Conclusion 121\u003c\/p\u003e \u003cp\u003eReferences 121\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Harmony Search 123\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 123\u003c\/p\u003e \u003cp\u003e10.1 Introduction 123\u003c\/p\u003e \u003cp\u003e10.2 Inspiration of the Harmony Search (HS) 124\u003c\/p\u003e \u003cp\u003e10.3 Initializing the Harmony Memory 125\u003c\/p\u003e \u003cp\u003e10.4 Generating New Harmonies (Solutions) 127\u003c\/p\u003e \u003cp\u003e10.4.1 Memory Strategy 127\u003c\/p\u003e \u003cp\u003e10.4.2 Random Selection 128\u003c\/p\u003e \u003cp\u003e10.4.3 Pitch Adjustment 129\u003c\/p\u003e \u003cp\u003e10.5 Updating the Harmony Memory 129\u003c\/p\u003e \u003cp\u003e10.6 Termination Criteria 130\u003c\/p\u003e \u003cp\u003e10.7 User- Defined Parameters of the HS 130\u003c\/p\u003e \u003cp\u003e10.8 Pseudocode of the HS 130\u003c\/p\u003e \u003cp\u003e10.9 Conclusion 131\u003c\/p\u003e \u003cp\u003eReferences 131\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Shuffled Frog\u003c\/b\u003e\u003cb\u003e-\u003c\/b\u003e\u003cb\u003eLeaping Algorithm 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 133\u003c\/p\u003e \u003cp\u003e11.1 Introduction 133\u003c\/p\u003e \u003cp\u003e11.2 Mapping Memetic Evolution of Frogs to the Shuffled Frog Leaping Algorithm (SFLA) 134\u003c\/p\u003e \u003cp\u003e11.3 Creating an Initial Population 137\u003c\/p\u003e \u003cp\u003e11.4 Classifying Frogs into Memeplexes 137\u003c\/p\u003e \u003cp\u003e11.5 Frog Leaping 138\u003c\/p\u003e \u003cp\u003e11.6 Shuffling Process 140\u003c\/p\u003e \u003cp\u003e11.7 Termination Criteria 141\u003c\/p\u003e \u003cp\u003e11.8 User-Defined Parameters of the SFLA 141\u003c\/p\u003e \u003cp\u003e11.9 Pseudocode of the SFLA 141\u003c\/p\u003e \u003cp\u003e11.10 Conclusion 142\u003c\/p\u003e \u003cp\u003eReferences 142\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Honey\u003c\/b\u003e\u003cb\u003e-\u003c\/b\u003e\u003cb\u003eBee Mating Optimization 145\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 145\u003c\/p\u003e \u003cp\u003e12.1 Introduction 145\u003c\/p\u003e \u003cp\u003e12.2 Mapping Honey-Bee Mating Optimization (HBMO) to the Honey- Bee Colony Structure 146\u003c\/p\u003e \u003cp\u003e12.3 Creating an Initial Population 148\u003c\/p\u003e \u003cp\u003e12.4 The Queen 150\u003c\/p\u003e \u003cp\u003e12.5 Drone Selection 150\u003c\/p\u003e \u003cp\u003e12.5.1 Mating Flights 151\u003c\/p\u003e \u003cp\u003e12.5.2 Trial Solutions 152\u003c\/p\u003e \u003cp\u003e12.6 Brood (New Solution) Production 152\u003c\/p\u003e \u003cp\u003e12.7 Improving Broods (New Solutions) by Workers 155\u003c\/p\u003e \u003cp\u003e12.8 Termination Criteria 156\u003c\/p\u003e \u003cp\u003e12.9 User-Defined Parameters of the HBMO 156\u003c\/p\u003e \u003cp\u003e12.10 Pseudocode of the HBMO 156\u003c\/p\u003e \u003cp\u003e12.11 Conclusion 158\u003c\/p\u003e \u003cp\u003eReferences 158\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Invasive Weed Optimization 163\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 163\u003c\/p\u003e \u003cp\u003e13.1 Introduction 163\u003c\/p\u003e \u003cp\u003e13.2 Mapping Invasive Weed Optimization (IWO) to Weeds’ Biology 164\u003c\/p\u003e \u003cp\u003e13.3 Creating an Initial Population 167\u003c\/p\u003e \u003cp\u003e13.4 Reproduction 167\u003c\/p\u003e \u003cp\u003e13.5 The Spread of Seeds 168\u003c\/p\u003e \u003cp\u003e13.6 Eliminating Weeds with Low Fitness 169\u003c\/p\u003e \u003cp\u003e13.7 Termination Criteria 170\u003c\/p\u003e \u003cp\u003e13.8 User- Defined Parameters of the IWO 170\u003c\/p\u003e \u003cp\u003e13.9 Pseudocode of the IWO 170\u003c\/p\u003e \u003cp\u003e13.10 Conclusion 171\u003c\/p\u003e \u003cp\u003eReferences 171\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Central Force Optimization 175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 175\u003c\/p\u003e \u003cp\u003e14.1 Introduction 175\u003c\/p\u003e \u003cp\u003e14.2 Mapping Central Force Optimization (CFO) to Newtons Gravitational Law 176\u003c\/p\u003e \u003cp\u003e14.3 Initializing the Position of Probes 177\u003c\/p\u003e \u003cp\u003e14.4 Calculation of Accelerations 180\u003c\/p\u003e \u003cp\u003e14.5 Movement of Probes 181\u003c\/p\u003e \u003cp\u003e14.6 Modification of Deviated Probes 181\u003c\/p\u003e \u003cp\u003e14.7 Termination Criteria 182\u003c\/p\u003e \u003cp\u003e14.8 User-Defined Parameters of the CFO 182\u003c\/p\u003e \u003cp\u003e14.9 Pseudocode of the CFO 183\u003c\/p\u003e \u003cp\u003e14.10 Conclusion 183\u003c\/p\u003e \u003cp\u003eReferences 183\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Biogeography\u003c\/b\u003e\u003cb\u003e-\u003c\/b\u003e\u003cb\u003eBased Optimization 185\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 185\u003c\/p\u003e \u003cp\u003e15.1 Introduction 185\u003c\/p\u003e \u003cp\u003e15.2 Mapping Biogeography-Based Optimization (BBO) to Biogeography Concepts 186\u003c\/p\u003e \u003cp\u003e15.3 Creating an Initial Population 188\u003c\/p\u003e \u003cp\u003e15.4 Migration Process 189\u003c\/p\u003e \u003cp\u003e15.5 Mutation 191\u003c\/p\u003e \u003cp\u003e15.6 Termination Criteria 192\u003c\/p\u003e \u003cp\u003e15.7 User- Defined Parameters of the BBO 192\u003c\/p\u003e \u003cp\u003e15.8 Pseudocode of the BBO 193\u003c\/p\u003e \u003cp\u003e15.9 Conclusion 193\u003c\/p\u003e \u003cp\u003eReferences 194\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Firefly Algorithm 195\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 195\u003c\/p\u003e \u003cp\u003e16.1 Introduction 195\u003c\/p\u003e \u003cp\u003e16.2 Mapping the Firefly Algorithm (FA) to the Flashing Characteristics of Fireflies 196\u003c\/p\u003e \u003cp\u003e16.3 Creating an Initial Population 198\u003c\/p\u003e \u003cp\u003e16.4 Attractiveness 199\u003c\/p\u003e \u003cp\u003e16.5 Distance and Movement 199\u003c\/p\u003e \u003cp\u003e16.6 Termination Criteria 200\u003c\/p\u003e \u003cp\u003e16.7 User-Defined Parameters of the FA 200\u003c\/p\u003e \u003cp\u003e16.8 Pseudocode of the FA 201\u003c\/p\u003e \u003cp\u003e16.9 Conclusion 201\u003c\/p\u003e \u003cp\u003eReferences 201\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Gravity Search Algorithm 203\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 203\u003c\/p\u003e \u003cp\u003e17.1 Introduction 203\u003c\/p\u003e \u003cp\u003e17.2 Mapping the Gravity Search Algorithm (GSA) to the Law of Gravity 204\u003c\/p\u003e \u003cp\u003e17.3 Creating an Initial Population 205\u003c\/p\u003e \u003cp\u003e17.4 Evaluation of Particle Masses 207\u003c\/p\u003e \u003cp\u003e17.5 UpdatingVelocities and Positions 207\u003c\/p\u003e \u003cp\u003e17.6 Updating Newton’s Gravitational Factor 208\u003c\/p\u003e \u003cp\u003e17.7 Termination Criteria 209\u003c\/p\u003e \u003cp\u003e17.8 User- Defined Parameters of the GSA 209\u003c\/p\u003e \u003cp\u003e17.9 Pseudocode of the GSA 209\u003c\/p\u003e \u003cp\u003e17.10 Conclusion 210\u003c\/p\u003e \u003cp\u003eReferences 210\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Bat Algorithm 213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 213\u003c\/p\u003e \u003cp\u003e18.1 Introduction 213\u003c\/p\u003e \u003cp\u003e18.2 Mapping the Bat Algorithm (BA) to the Behavior of Microbats 214\u003c\/p\u003e \u003cp\u003e18.3 Creating an Initial Population 215\u003c\/p\u003e \u003cp\u003e18.4 Movement of Virtual Bats 217\u003c\/p\u003e \u003cp\u003e18.5 Local Search and Random Flying 218\u003c\/p\u003e \u003cp\u003e18.6 Loudness and Pulse Emission 218\u003c\/p\u003e \u003cp\u003e18.7 Termination Criteria 219\u003c\/p\u003e \u003cp\u003e18.8 User-Defined Parameters of the BA 219\u003c\/p\u003e \u003cp\u003e18.9 Pseudocode of the BA 219\u003c\/p\u003e \u003cp\u003e18.10 Conclusion 220\u003c\/p\u003e \u003cp\u003eReferences 220\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Plant Propagation Algorithm 223\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 223\u003c\/p\u003e \u003cp\u003e19.1 Introduction 223\u003c\/p\u003e \u003cp\u003e19.2 Mapping the Natural Process to the Planet Propagation Algorithm (PPA) 223\u003c\/p\u003e \u003cp\u003e19.3 Creating an Initial Population of Plants 226\u003c\/p\u003e \u003cp\u003e19.4 Normalizing the Fitness Function 226\u003c\/p\u003e \u003cp\u003e19.5 Propagation 227\u003c\/p\u003e \u003cp\u003e19.6 Elimination of Extra Solutions 228\u003c\/p\u003e \u003cp\u003e19.7 Termination Criteria 228\u003c\/p\u003e \u003cp\u003e19.8 User-Defined Parameters of the PPA 228\u003c\/p\u003e \u003cp\u003e19.9 Pseudocode of the PPA 229\u003c\/p\u003e \u003cp\u003e19.10 Conclusion 230\u003c\/p\u003e \u003cp\u003eReferences 230\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Water Cycle Algorithm 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 231\u003c\/p\u003e \u003cp\u003e20.1 Introduction 231\u003c\/p\u003e \u003cp\u003e20.2 Mapping the Water Cycle Algorithm (WCA) to the Water Cycle 232\u003c\/p\u003e \u003cp\u003e20.3 Creating an Initial Population 233\u003c\/p\u003e \u003cp\u003e20.4 Classification of Raindrops 235\u003c\/p\u003e \u003cp\u003e20.5 Streams Flowing to the Rivers or Sea 236\u003c\/p\u003e \u003cp\u003e20.6 Evaporation 237\u003c\/p\u003e \u003cp\u003e20.7 Raining Process 238\u003c\/p\u003e \u003cp\u003e20.8 Termination Criteria 239\u003c\/p\u003e \u003cp\u003e20.9 User-Defined Parameters of the WCA 239\u003c\/p\u003e \u003cp\u003e20.10 Pseudocode of the WCA 239\u003c\/p\u003e \u003cp\u003e20.11 Conclusion 240\u003c\/p\u003e \u003cp\u003eReferences 240\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Symbiotic Organisms Search 241\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 241\u003c\/p\u003e \u003cp\u003e21.1 Introduction 241\u003c\/p\u003e \u003cp\u003e21.2 Mapping Symbiotic Relations to the Symbiotic Organisms Search (SOS) 241\u003c\/p\u003e \u003cp\u003e21.3 Creating an Initial Ecosystem 242\u003c\/p\u003e \u003cp\u003e21.4 Mutualism 244\u003c\/p\u003e \u003cp\u003e21.5 Commensalism 245\u003c\/p\u003e \u003cp\u003e21.6 Parasitism 245\u003c\/p\u003e \u003cp\u003e21.7 Termination Criteria 246\u003c\/p\u003e \u003cp\u003e21.8 Pseudocode of the SOS 246\u003c\/p\u003e \u003cp\u003e21.9 Conclusion 247\u003c\/p\u003e \u003cp\u003eReferences 247\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Comprehensive Evolutionary Algorithm 249\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummary 249\u003c\/p\u003e \u003cp\u003e22.1 Introduction 249\u003c\/p\u003e \u003cp\u003e22.2 Fundamentals of the Comprehensive Evolutionary Algorithm (CEA) 250\u003c\/p\u003e \u003cp\u003e22.3 Generating an Initial Population of Solutions 253\u003c\/p\u003e \u003cp\u003e22.4 Selection 253\u003c\/p\u003e \u003cp\u003e22.5 Reproduction 255\u003c\/p\u003e \u003cp\u003e22.5.1 Crossover Operators 255\u003c\/p\u003e \u003cp\u003e22.5.2 Mutation Operators 261\u003c\/p\u003e \u003cp\u003e22.6 Roles of Operators 262\u003c\/p\u003e \u003cp\u003e22.7 Input Data to the CEA 263\u003c\/p\u003e \u003cp\u003e22.8 Termination Criteria 264\u003c\/p\u003e \u003cp\u003e22.9 Pseudocode of the CEA 265\u003c\/p\u003e \u003cp\u003e22.10 Conclusion 265\u003c\/p\u003e \u003cp\u003eReferences 266\u003c\/p\u003e \u003cp\u003eWiley Series in Operations Research and Management Science 267\u003c\/p\u003e \u003cp\u003eIndex 269\u003c\/p\u003e \u003cp\u003e\u003cb\u003eOmid Bozorg-Haddad, PhD,\u003c\/b\u003e is Professor in the Department of Irrigation and Reclamation Engineering at the University of Tehran, Iran.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eMohammad Solgi, M.Sc.,\u003c\/b\u003e is Teacher Assistant for M.Sc. courses at the University of Tehran, Iran.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eHugo A. Loáiciga, PhD,\u003c\/b\u003e is Professor in the Department of Geography at the University of California, Santa Barbara, United States of America.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA detailed review of a wide range of meta-heuristic and evolutionary algorithms in a systematic manner and how they relate to engineering optimization problems\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis book introduces the main metaheuristic algorithms and their applications in optimization. It describes 20 leading meta-heuristic and evolutionary algorithms and presents discussions and assessments of their performance in solving optimization problems from several fields of engineering. The book features clear and concise principles and presents detailed descriptions of leading methods such as the pattern search (PS) algorithm, the genetic algorithm (GA), the simulated annealing (SA) algorithm, the Tabu search (TS) algorithm, the ant colony optimization (ACO), and the particle swarm optimization (PSO) technique.\u003c\/p\u003e \u003cp\u003eChapter 1 of \u003ci\u003eMeta-heuristic and Evolutionary Algorithms for Engineering Optimization\u003c\/i\u003e provides an overview of optimization and defines it by presenting examples of optimization problems in different engineering domains. Chapter 2 presents an introduction to meta-heuristic and evolutionary algorithms and links them to engineering problems. Chapters 3 to 22 are each devoted to a separate algorithm and they each start with a brief literature review of the development of the algorithm, and its applications to engineering problems. The principles, steps, and execution of the algorithms are described in detail, and a pseudo code of the algorithm is presented, which serves as a guideline for coding the algorithm to solve specific applications. This book:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eIntroduces state-of-the-art metaheuristic algorithms and their applications to engineering optimization;\u003c\/li\u003e \u003cli\u003eFills a gap in the current literature by compiling and explaining the various meta-heuristic and evolutionary algorithms in a clear and systematic manner;\u003c\/li\u003e \u003cli\u003eProvides a step-by-step presentation of each algorithm and guidelines for practical implementation and coding of algorithms;\u003c\/li\u003e \u003cli\u003eDiscusses and assesses the performance of metaheuristic algorithms in multiple problems from many fields of engineering;\u003c\/li\u003e \u003cli\u003eRelates optimization algorithms to engineering problems employing a unifying approach.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eMeta-heuristic and Evolutionary Algorithms for Engineering Optimization\u003c\/i\u003e is a reference intended for students, engineers, researchers, and instructors in the fields of industrial engineering, operations research, optimization\/mathematics, engineering optimization, and computer science.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989610152165,"sku":"NP9781119386995","price":144.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119386995.jpg?v=1761784798","url":"https:\/\/k12savings.com\/es\/products\/meta-heuristic-and-evolutionary-algorithms-for-engineering-optimization-isbn-9781119386995","provider":"K12savings","version":"1.0","type":"link"}